Real-world evidence in the cloud: Tutorial on developing an end-to-end data and analytics pipeline using Amazon Web Services resources.

In the rapidly evolving landscape of healthcare and drug development, the ability to efficiently collect, process, and analyze large volumes of real-world data (RWD) is critical for advancing drug development. This article provides a blueprint for establishing an end-to-end data and analytics pipeline in a cloud-based environment. The pipeline presented here includes four major components, including data ingestion, transformation, visualization, and analytics, each supported by a suite of Amazon Web Services (AWS) tools.

Type 1 diabetes prevention clinical trial simulator: Case reports of model-informed drug development tool.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA-approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model-based clinical trial simulation tool.

Artificial Intelligence: From Buzzword to Useful Tool in Clinical Pharmacology.

The advent of artificial intelligence (AI) in clinical pharmacology and drug development is akin to the dawning of a new era. Previously dismissed as merely technological hype, these approaches have emerged as promising tools in different domains, including health care, demonstrating their potential to empower clinical pharmacology decision making, revolutionize the drug development landscape, and advance patient care. Although challenges remain, the remarkable progress already made signals that the leap from hype to reality is well underway, and AI promises to offer clinical pharmacology new tools and possibilities for optimizing patient care is gradually coming to fruition.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes.

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials.

T1dCteGui: A User-Friendly Clinical Trial Enrichment Tool to Optimize T1D Prevention Studies by Leveraging AI/ML Based Synthetic Patient Population.

Whereas islet autoantibodies (AAs) are well-established risk factors for developing type 1 diabetes (T1D), there is a lack of biomarkers endorsed by regulators to enrich clinical trial populations for those at risk of developing T1D. As such, the development of therapies that delay or prevent the onset of T1D remains challenging. To address this drug development need, the Critical Path Institute's T1D Consortium (T1DC) acquired patient-level data from multiple observational studies and used a model-based approach to evaluate the utility of islet AAs as enrichment biomarkers in clinical trials.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies.

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations.

How can natural language processing help model informed drug development?: a review.

Objective: To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas of improvement.

Materials And Methods: Publications found on PubMed and Google Scholar, websites and GitHub repositories for NLP libraries and models. Publications describing applications of NLP in MIDD were reviewed.

Leveraging Real-World Data for EMA Qualification of a Model-Based Biomarker Tool to Optimize Type-1 Diabetes Prevention Studies.

The development of therapies to prevent or delay the onset of type 1 diabetes (T1D) remains challenging, and there is a lack of qualified biomarkers to identify individuals at risk of developing T1D or to quantify the time-varying risk of conversion to a diagnosis of T1D. To address this drug development need, the T1D Consortium (i) acquired, remapped, integrated, and curated existing patient-level data from relevant observational studies, and (ii) used a model-based approach to evaluate the utility of islet autoantibodies (AAs) against insulin/proinsulin autoantibody, GAD65, IA-2, and ZnT8 as biomarkers to enrich subjects for T1D prevention. The aggregated dataset was used to construct an accelerated failure time model for predicting T1D diagnosis.

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials.

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS).

Machine Learning in Drug Discovery and Development Part 1: A Primer.

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example.

Pharmacokinetics of Basiliximab for the Prevention of Graft-versus-Host Disease in Patients Undergoing Hematopoietic Cell Transplantation with Minimal-Intensity Cyclophosphamide and Fludarabine.

Study Objective: Basiliximab is an immunosuppressive monoclonal antibody used for rejection prevention following solid organ transplantation; the pharmacokinetics (PK) of basiliximab in this setting are known. Basiliximab may also be used for prophylaxis and treatment of graft-versus-host disease (GVHD) in patients undergoing allogeneic hematopoietic cell transplantation (HCT); however, the PK of basiliximab in this setting are not known. Clinical transplant providers expect variation in the volume of distribution and clearance after nonmyeloablative allogeneic transplantation (NMAT) compared with solid organ transplantation.

The feasibility of genome-scale biological network inference using Graphics Processing Units.

Systems research spanning fields from biology to finance involves the identification of models to represent the underpinnings of complex systems. Formal approaches for data-driven identification of network interactions include statistical inference-based approaches and methods to identify dynamical systems models that are capable of fitting multivariate data. Availability of large data sets and so-called 'big data' applications in biology present great opportunities as well as major challenges for systems identification/reverse engineering applications.

Multiple approaches to predicting oxygen and glucose consumptions by HepG2 cells on porous scaffolds in an axial-flow bioreactor.

In this study, the distribution of oxygen and glucose was evaluated along with consumption by hepatocytes using three different approaches. The methods include (i) Computational Fluid Dynamics (CFD) simulation, (ii) residence time distribution (RTD) analysis using a step-input coupled with segregation model or dispersion model, and (iii) experimentally determined consumption by HepG2 cells in an open-loop. Chitosan-gelatin (CG) scaffolds prepared by freeze-drying and polycaprolactone (PCL) scaffolds prepared by salt leaching technique were utilized for RTD analyses.

Modeling pressure drop using generalized scaffold characteristics in an axial-flow bioreactor for soft tissue regeneration.

The goal of this study was to better understand how analytical permeability models based on scaffold architecture can facilitate a non-invasive technique to real time monitoring of pressure drop in bioreactors. In particular, we evaluated the permeability equations for electrospun and freeze dried scaffolds via pressure drop comparison in an axial-flow bioreactor using computational fluid dynamic (CFD) and experimentation. The polycaprolactone-cellulose acetate fibers obtained by co-axial electrospinning technique and Chitosan-Gelatin scaffolds prepared using freeze-drying techniques were utilized.

Modeling of porous scaffold deformation induced by medium perfusion.

In this study, we tested the possibility of calculating permeability of porous scaffolds utilized in soft tissue engineering using pore size and shape. We validated the results using experimental measured pressure drop and simulations with the inclusion of structural deformation. We prepared Polycaprolactone (PCL) and Chitosan-Gelatin (CG) scaffolds by salt leaching and freeze drying technique, respectively.

Application of computational fluid dynamics in tissue engineering.

The process of tissue regeneration consists of a set of complex phenomena such as hydrodynamics, nutrient transfer, cell growth, and matrix deposition. Traditional cell culture and bioreactor design procedure follow trial-and-error analyses to understand the effects of varying physical, chemical, and mechanical parameters that govern the process of tissue regeneration. This trend has been changing as computational fluid dynamics (CFD) analysis can now be used to understand the effects of flow, cell proliferation, and consumption kinetics on the dynamics involved with in vitro tissue regeneration.

Dynamics of diffusivity and pressure drop in flow-through and parallel-flow bioreactors during tissue regeneration.

In this study, transport characteristics in flow-through and parallel-flow bioreactors used in tissue engineering were simulated using computational fluid dynamics. To study nutrient distribution and consumption by smooth muscle cells colonizing the 100 mm diameter and 2-mm thick scaffold, effective diffusivity of glucose was experimentally determined using a two-chambered setup. Three different concentrations of chitosan-gelatin scaffolds were prepared by freezing at -80°C followed by lyophilization.